Slingshot: Cell Lineage and Pseudotime Inference for Single-Cell Transcriptomics

• Activity Number: 418 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Kelly Street* and David Risso and Russell B. Fletcher and Diya Das and John Ngai and Nir Yosef and Elizabeth Purdom and Sandrine Dudoit
• Companies: Univ of California - Berkeley and Department of Healthcare Policy and Research, Weill Cornell Medicine and Department of Molecular and Cell Biology, UC Berkeley and Department of Molecular and Cell Biology, UC Berkeley and Department of Molecular and Cell Biology & Helen Wills Nenuroscience Institute, UC Berkeley and Department of Electrical Engineering and Computer Sciences, UC Berkeley and Department of Statistics, UC Berkeley; Center for Computational Biology, UC Berkeley and Division of Biostatistics, School of Public Health, UC Berkeley; Department of Statistics
• Keywords: Single-Cell ; Transcriptomics ; RNA-Seq ; Pseudotime

Abstract

Newly developed single-cell transcriptomics methods afford researchers a highly detailed view of gene expression. One common target for these methods has been stem cells and their descendants, with analyses focused on charting the progression from multipotent cells to mature, differentiated populations. We introduce Slingshot, a novel statistical method for inferring multiple developmental lineages from single-cell gene expression data. Slingshot is a uniquely robust and flexible tool for inferring developmental lineages and ordering cells to reflect continuous differentiation processes. We demonstrate these advantages on existing and simulated data with different levels of noise and numbers of distinct terminal states. The method is implemented in the open-source R package "slingshot," to be released through the Bioconductor Project.